Feature-Based Matcher for Distorted Fingerprint Matching

ABSTRACT

In one aspect, methods that use a novel representation, referred to as an octant feature vector (OFV), are used for matching distorted fingerprints. For instance, a feature-based matcher for distorted fingerprint matching may use a two-step local and global matching scheme to compare a set of feature vectors that are derived from minutiae of a reference fingerprint and a search fingerprint. The relative geometric relationships between the reference minutia and nearest minutiae may be derived and encoded into a feature vector based on orientation difference. The OFV is invariant to the rigid transformations and is insensitive to nonlinear distortions since the relative geometric relationships are independent from the rigid transformation.

FIELD

The present disclosure relates generally to fingerprint identificationsystems.

BACKGROUND

Pattern matching systems such as ten-print or fingerprint matchingsystems play a critical role in criminal and civil applications. Forexample, fingerprint identification is often used for identify and tracksuspects and in criminal investigations. Similarly, fingerprintverification is used in in civil applications to prevent fraud andsupport other security processes.

SUMMARY

In general, the matching of distorted fingerprints pose severalchallenges to fingerprint identification and matching technologies. Forinstance, distortions can impact matching accuracy because thedistortions may change both the geometric locations and orientations ofindividual minutiae.

Accordingly, one innovative aspect of the subject matter describedthroughout this specification involves methods that use a novelrepresentation, referred to as an octant feature vector (OFV), formatching distorted fingerprints. For instance, a feature-based matcherfor distorted fingerprint matching may use a two-step local and globalmatching scheme to compare a set of feature vectors that are derivedfrom minutiae of a reference fingerprint and a search fingerprint. Therelative geometric relationships between the reference minutia andnearest minutiae may be derived and encoded into a feature vector basedon orientation difference. The OFV is invariant to the rigidtransformations and is insensitive to nonlinear distortions since therelative geometric relationships are independent from the rigidtransformation. The OFVs that are generated for individual minutiae maybe compared to compute a similarity between the reference and searchfingerprint.

Implementations may include one or more of the following features. Forexample, a method for matching distorted fingerprints, said methodimplemented by an automatic fingerprint identification system includinga processor, a memory coupled to the processor, an interface to afingerprint scanning device, and a sensor associated with thefingerprint scanning device that indicates a fingerprint match. Themethod may include: receiving (i) a plurality of search octant featurevectors associated with a plurality of search minutiae extracted from asearch fingerprint, and (ii) a plurality of reference octant featurevectors associated with a plurality of reference minutiae extracted froma reference fingerprint, where the search fingerprint includes one ormore non-overlapping sectors that each include a subset of the pluralityof search minutiae, and each of the plurality of search octant featurevectors includes a set of relative features that represent a differencebetween a particular search minutia and a plurality of particularreference minutiae that are identified as closest neighboring minutiaewithin a particular sector that includes the particular search minutiae,and each of the plurality of reference octant feature vectors includes aset of relative features that represent a difference between aparticular reference minutia and a plurality of particular searchminutiae that are identified as closest neighboring minutiae within aparticular sector that includes the particular search minutiae;

The computer-implemented method may include: computing, for each of theplurality of search minutiae, a first score that (i) represents thedifference between the particular search octant feature associated withthe particular search minutia and the plurality of reference octantfeature vectors associated with a plurality of particular referenceminutiae that are identified as closest neighboring minutiae within theparticular sector that includes the particular search minutiae, and (ii)is adjusted based at least on determining that at least one of theplurality of particular reference minutiae that are identified as theclosest neighboring minutiae within the particular sector that includesthe particular search minutia has drifted to another of the one or moresectors due to distortion within the search fingerprint.

The computer-implemented method may include: computing, for each of theplurality of search minutiae, a second score that represents a ratioindicating a number of paired mated minutiae relative to a number ofpaired unmated minutiae all of the one or more sectors, where: thepaired mated minutiae represent the particular search minutia that havethe plurality of particular reference minutiae that are identified asclosest neighboring minutiae within each of the one or more sectors, andthe paired unmated minutiae represent the particular search minutia thatdo not have at least one particular reference minutia that is identifiedas the closest neighboring minutiae within each of the one or moresectors.

The computer-implemented method may include: computing, for each of theplurality of search minutiae, a local similarity score based at least oncombining the first score and the second score; determining, based atleast on the computed local similarity scores for each of the pluralityof search minutiae, (i) a geometrically aligned region between thesearch fingerprint and the reference fingerprint, and (ii) a rotationangle between the search fingerprint and the reference fingerprint; andidentifying a plurality of globally aligned mated minutiae within thegeometrically aligned region, where the plurality of globally alignedmated minutiae represent a set of particular search minutia that havegeometrically consistent features, determined based at least on therotation angle, to a plurality of particular reference minutiae withinthe geometrically aligned region.

The computer-implemented method may include: computing, for each of theplurality of globally aligned mated minutiae, an additional first scorethat represents the difference between a particular search octantfeature associated with the particular search minutia within thegeometrically aligned region and a plurality of reference octant featurevectors associated with the plurality of particular reference minutiaewithin the geometrically aligned region; and computing, for each of theplurality of globally aligned mated minutiae, an additional second scorethat represents a ratio indicating a number of paired globally alignedmated minutiae relative to a number of globally aligned unmated minutiaewithin geometrically aligned region.

The computer-implemented method may include: computing (i) a third scorebased at least on combining the respective first scores for each of theplurality of search minutiae, and the respective additional first scoresfor each of the plurality of globally aligned mated minutiae, (ii) afourth score based at least on combining the respective second scoresfor each of the plurality of search minutiae, and the respectiveadditional second scores for each of the plurality of globally alignedmated minutiae; computing a match similarity score between the searchfingerprint and the reference fingerprint based at least on combiningthe third score and the fourth score; and providing the match similarityscore for output to the automatic fingerprint identification system.

Other versions include corresponding systems, and computer programs,configured to perform the actions of the methods encoded on computerstorage devices.

One or more implementations may include the following optional features.For example, in some implementations, combining the third score and thefourth score includes adding the values of the respective scores.

In some implementations, identifying the plurality of globally alignedmated minutiae within the geometrically aligned region includes:obtaining a plurality of local closest matched minutia pairs based on aplurality of top matched local similarity scores; and computing aplurality sets of rotation parameters based on the plurality of thelocal closest matched minutia pairs.

In some implementations, identifying the plurality of globally alignedmated minutiae within the geometrically aligned region includes:computing (i) a set of rotation parameters, and (ii) a set oftranslation parameters, based on the plurality of globally aligned matedminutiae within the geometrically aligned region; and aligning thesearch minutiae to the reference minutiae based at least on set ofrotation parameters and the set of translation parameters.

In some implementations, computing the plurality sets of rotationparameters includes: identifying a plurality of angles corresponding toa plurality of top bins of an angle offset histogram of the plurality ofthe local closest matched minutiae pairs.

In some implementations, prior to identifying the plurality of angles,generating the angle offset histogram of the plurality local matchedminutia pairs using a smoothing process.

In some implementations, identifying the plurality of globally alignedmated minutiae within the geometrically aligned region includes:applying a two-stage pruning procedure to perform local and globalpairing operations on the local matched minutia pairs.

In some implementations, the local and global pairing operations areperformed using a plurality of rotations and the plurality of topclosest matched minutia pairs to remove: a subset of the plurality ofglobally aligned mated minutiae that are identified to not be the localclosest matched pairs, a subset of the plurality of globally alignedmated minutiae with lower local similarity scores within a same index,or a subset of the plurality of globally aligned mated minutiae withtransformed pairs for the corresponding rotations that do not yield thehighest cumulative local similarity scores.

In some implementations, the match similarity score is used to determinea fingerprint match between the search fingerprint and the referencefingerprint, where the search fingerprint is a distorted fingerprint.

In some implementations, the computer-implemented method includesdetermining, within a particular fingerprint identification operation, amatch between the search fingerprint and the reference fingerprint basedat least on the value of the match similarity score.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other potentialfeatures and advantages will become apparent from the description, thedrawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of an exemplary automatic fingerprintidentification system.

FIG. 1B is a block diagram of an exemplary feature extraction process.

FIG. 2 is an exemplary illustration of geometric relationships between areference minutia and a neighboring minutia.

FIG. 3 is a graphical illustration of the relationships represented inan exemplary octant feature vector (OFV).

FIG. 4 is an exemplary process of generating an octant feature vector(OFV).

FIG. 5 is an exemplary process of calculating similarity between twominutiae.

FIG. 6 is an exemplary alignment process for a pair of fingerprints.

FIG. 7 is an exemplary minutiae matching process for a pair offingerprints.

FIG. 8 is an exemplary process of distorted fingerprint matching.

In the drawings, like reference numbers represent corresponding partsthroughout.

DETAILED DESCRIPTION

In general, the matching of distorted fingerprints pose severalchallenges to fingerprint identification and matching technologies. Forinstance, distortions can impact matching accuracy because thedistortions may change both the geometric locations and orientations ofindividual minutiae.

System Architecture

FIG. 1 is a block diagram of an exemplary automatic fingerprintidentification system 100. Briefly, the automatic fingerprintidentification system 100 may include a computing device including amemory device 110, a processor 115, a presentation interface 120, a userinput interface 130, and a communication interface 135. The automaticfingerprint identification system 100 may be configured to facilitateand implement the methods described through this specification. Inaddition, the automatic fingerprint identification system 100 mayincorporate any suitable computer architecture that enables operationsof the system described throughout this specification.

The processor 115 may be operatively coupled to memory device 110 forexecuting instructions. In some implementations, executable instructionsare stored in the memory device 110. For instance, the automaticfingerprint identification system 100 may be configurable to perform oneor more operations described by programming the processor 115. Forexample, the processor 115 may be programmed by encoding an operation asone or more executable instructions and providing the executableinstructions in the memory device 110. The processor 115 may include oneor more processing units, e.g., without limitation, in a multi-coreconfiguration.

The memory device 110 may be one or more devices that enable storage andretrieval of information such as executable instructions and/or otherdata. The memory device 110 may include one or more tangible,non-transitory computer-readable media, such as, without limitation,random access memory (RAM), dynamic random access memory (DRAM), staticrandom access memory (SRAM), a solid state disk, a hard disk, read-onlymemory (ROM), erasable programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), and/or non-volatile RAM (NVRAM) memory. Theabove memory types are exemplary only, and are thus not limiting as tothe types of memory usable for storage of a computer program.

The memory device 110 may be configured to store a variety of dataincluding, for example, matching algorithms, scoring algorithms, scoringthresholds, perturbation algorithms, fusion algorithms, virtual minutiaegeneration algorithms, minutiae overlap analysis algorithms, and/orvirtual minutiae analysis algorithms. In addition, the memory device 110may be configured to store any suitable data to facilitate the methodsdescribed throughout this specification.

The presentation interface 120 may be coupled to processor 115. Forinstance, the presentation interface 120 may present information, suchas a user interface showing data related to fingerprint matching, to auser 102. For example, the presentation interface 120 may include adisplay adapter (not shown) that may be coupled to a display device (notshown), such as a cathode ray tube (CRT), a liquid crystal display(LCD), an organic LED (OLED) display, and/or a hand-held device with adisplay. In some implementations, the presentation interface 120includes one or more display devices. In addition, or alternatively, thepresentation interface 120 may include an audio output device (notshown), e.g., an audio adapter and/or a speaker.

The user input interface 130 may be coupled to the processor 115 andreceives input from the user 102. The user input interface 130 mayinclude, for example, a keyboard, a pointing device, a mouse, a stylus,and/or a touch sensitive panel, e.g., a touch pad or a touch screen. Asingle component, such as a touch screen, may function as both a displaydevice of the presentation interface 120 and the user input interface130.

In some implementations, the user input interface 130 may represent afingerprint scanning device that is used to capture and recordfingerprints associated with a subject (e.g., a human individual) from aphysical scan of a finger, or alternately, from a scan of a latentprint. In addition, the user input interface 130 may be used to create aplurality of reference records.

A communication interface 135 may be coupled to the processor 115 andconfigured to be coupled in communication with one or more other devicessuch as, for example, another computing system (not shown), scanners,cameras, and other devices that may be used to provide biometricinformation such as fingerprints to the automatic fingerprintidentification system 100. Such biometric systems and devices may beused to scan previously captured fingerprints or other image data or tocapture live fingerprints from subjects. The communication interface 135may include, for example, a wired network adapter, a wireless networkadapter, a mobile telecommunications adapter, a serial communicationadapter, and/or a parallel communication adapter. The communicationinterface 135 may receive data from and/or transmit data to one or moreremote devices. The communication interface 135 may be also beweb-enabled for remote communications, for example, with a remotedesktop computer (not shown).

The presentation interface 120 and/or the communication interface 135may both be capable of providing information suitable for use with themethods described throughout this specification, e.g., to the user 102or to another device. In this regard, the presentation interface 120 andthe communication interface 135 may be used to as output devices. Inother instances, the user input interface 130 and the communicationinterface 135 may be capable of receiving information suitable for usewith the methods described throughout this specification, and may beused as input devices.

The processor 115 and/or the memory device 110 may also be operativelycoupled to the database 150. The database 150 may be anycomputer-operated hardware suitable for storing and/or retrieving data,such as, for example, pre-processed fingerprints, processedfingerprints, normalized fingerprints, extracted features, extracted andprocessed feature vectors such as octant feature vectors (OFVs),threshold values, virtual minutiae lists, minutiae lists, matchingalgorithms, scoring algorithms, scoring thresholds, perturbationalgorithms, fusion algorithms, virtual minutiae generation algorithms,minutiae overlap analysis algorithms, and virtual minutiae analysisalgorithms.

The database 150 may be integrated into the automatic fingerprintidentification system 100. For example, the automatic fingerprintidentification system 100 may include one or more hard disk drives thatrepresent the database 150. In addition, for example, the database 150may include multiple storage units such as hard disks and/or solid statedisks in a redundant array of inexpensive disks (RAID) configuration. Insome instances, the database 150 may include a storage area network(SAN), a network attached storage (NAS) system, and/or cloud-basedstorage. Alternatively, the database 150 may be external to theautomatic fingerprint identification system 100 and may be accessed by astorage interface (not shown). For instance, the database 150 may beused to store various versions of reference records including associatedminutiae, octant feature vectors (OFVs) and associated data related toreference records.

Feature Extraction

In general, feature extraction describes the process by which theautomatic fingerprint detection system 100 extracts a list of minutiaefrom each of reference fingerprint, and the search fingerprint. Asdescribed, a “minutiae” represent major features of a fingerprint, whichare used in comparisons of the reference fingerprint to the searchfingerprint to determine a fingerprint match. For example, common typesof minutiae may include, for example, a ridge ending, a ridgebifurcation, a short ridge, an island, a ridge enclosure, a spur, acrossover or bridge, a delta, or a core.

FIG. 1B is a block diagram of an exemplary feature extraction process150. As shown, after receiving an input fingerprint 104, the automaticfingerprint detection system 100 initially identifies a set of features112 within the fingerprint, generates a list of minutiae 114, andextracts a set of feature vectors 116. For instance, the automaticfingerprint detection system 100 may generate a list of minutia 114 foreach of the reference fingerprint (or “reference record”) and a searchfingerprint (or “search record”).

In some implementations, the feature vectors 116 may be described usingfeature vector that is represented by Mf_(i)=(x_(i), y_(i), θ_(i)). Asdescribed, the feature vector Mf_(i) includes a minutia location that isdefined by coordinate geometry such as (x_(i),y_(i)), and a minutiaedirection that is defined by the angle θ_(i) ε[0,2π]. In other examples,further minutiae characteristics such as, quality, ridge frequency, andridge curvature may also be used to describe feature vector Mf_(i). Theextracted feature vectors may be used to generate octant feature vectors(OFVs) for each of identified minutia within the search and referencefingerprints.

Octant Feature Vector (OFV) Overview

The automatic fingerprint identification system 100 may compare thesearch and reference records based on initially generating featurevectors associated with minutiae that are extracted from the search andreference records, respectively. For instance, as described throughoutthis specification, in some implementations, octant feature vectors(OFVs) may be used to as feature vectors that define attributes of theextracted minutiae. However, in other implementations, other minutiaedescriptors may be used.

OFVs encode geometric relationships between reference minutiae and thenearest neighboring minutiae to the reference minutiae in a particularsector (referred to as the “octant neighborhood”) of the octant. Eachsector of the octant used in an OFV spans 45 degrees of a fingerprintregion. The nearest neighboring minutiae may be assigned to one sectorof the octant based on their orientation difference. The geometricrelationship between a reference minutia and its nearest minutia in eachoctant sector may be described by relative features including, forexample, distance between the minutiae and the orientation differencebetween minutiae. The representation achieved by the use of an OFV isinvariant to transformation. In addition, this representation isinsensitive to a nonlinear distortion because the relative features areindependent from any transformation.

Pairs of reference minutiae and nearest neighboring minutiae may beidentified as “mated minutiae pairs.” The mated minutiae pairs in areference record and a search record may be identified by comparing therespective OFVs of minutiae extracted from the reference record and thesearch record. The transformation parameters may be estimated bycomparing attributes of the corresponding mated minutiae. For example,the transformation parameters may indicate the degree to which thesearch record has been transformed (e.g., perturbed or twisted) asrelative to a particular reference record. In other examples, thetransformation parameters may be applied to verify that, for aparticular pair of a reference record and a search record (a “potentialmatched fingerprint pair”), mated minutiae pairs exhibit correspondingdegrees of transformation. Based on the amount of corresponding matedminutiae pairs in each potential matched fingerprint pair, and theconsistency of the transformation, a similarity score may be assigned.In some implementations, the pair of potential matched fingerprint pairswith the highest similarity score may be determined as a candidatematched fingerprint pair.

The automatic fingerprint identification system 100 may calculate an OFVfor each minutia that encodes the geometric relationships between thereference minutia and its nearest minutiae in each sector of the octant.For instance, the automatic fingerprint identification system 100 maydefine eight octant sectors and assigns the nearest minutiae to onesector of the octant based on the location of each minutiae within thesectors. The geometric relationship between a reference minutia and itsnearest minutia in each octant sector is represented by the relativefeatures. For example, in some implementations, the OFV encodes thedistance, the orientation difference, and the ridge count differencebetween the reference feature and the nearest neighbor features. Becausethe minutia orientation can flexibly change up to 45° due to the octantsector approach, relative features are independent from anytransformation.

The automatic fingerprint identification system 100 may use the OFVs todetermine the number of possible corresponding minutiae pairs.Specifically, the automatic fingerprint identification system 100 mayevaluate the similarity between two respective OFVs associated with thesearch record and the file record. The automatic fingerprintidentification system 100 may identify all possible local matching areasof the compared fingerprints by comparing the OFVs. The automaticfingerprint identification system 100 may also an individual similarityscore for each of the mated OFV pairs.

The automatic fingerprint identification system 100 may clusters allOFVs of the matched areas with similar transformation effects (e.g.,rotation and transposition) into an associated similar bin. Note thatthe precision of the clusters of the bins (e.g., the variance of thesimilar rotations within each bin) is a proxy for the precision of thisphase. Automatic fingerprint identification system 100 therefore usesbins with higher numbers of matched OFVs (e.g., clusters with thehighest counts of OFVs) for the first phase global alignment.

The automatic fingerprint identification system 100 may use the locationand angle of each selected bin as the parameters of a reference point(an “anchor point”) to perform a global alignment procedure. Morespecifically, the automatic fingerprint identification system 100 mayidentify the global alignment based on the bins that include thegreatest number of the global mated minutiae pairs, and the location andangle associated with each of those bins. Based on the number of globalpaired minutiae found and the total of individual similarity scorescalculated for the corresponding OFVs within the bin or bins, theautomatic fingerprint identification system 100 may identify thetransformations (e.g., the rotations of the features) with the bestalignment.

In a second phase, the automatic fingerprint identification system 100performs a more precise pairing using the transformations with the bestalignment to obtain a final set of the globally aligned minutiae pairs.In this phase, automatic fingerprint identification system 100 performsa pruning procedure to find geometrically consistent minutiae pairs withtolerance of distortion for each aligned minutiae set that factors inthe local and global geometrical index consistency. By performing suchalignment globally and locally, automatic fingerprint identificationsystem 100 determines the best set of global aligned minutiae pairs.Automatic fingerprint identification system 100 uses the associatedmini-scores of the global aligned pairs to calculate the globalsimilarity score. Furthermore, automatic fingerprint identificationsystem 100 factors in a set of absolute features of the minutiae,including the quality, ridge frequency, and the curvatures in thecomputation of the final similarity score.

OFV Generation

The automatic fingerprint identification system 100 may generate anoctant feature vector (OFV) for each minutia of the features extracted.Specifically, as described above, the automatic fingerprintidentification system 100 may generate OFVs encoding the distance andthe orientation difference between the reference minutiae and thenearest neighbor in each of eight octant sectors. Alternately, theautomatic fingerprint identification system 100 may generate featurevectors with different numbers of sectors.

FIG. 2 is an exemplary illustration 200 of geometric relationshipsbetween a reference minutia 210 and a neighboring minutia 220. Thegeometric relationships may be used to construct a rotation andtranslation invariant feature vector that includes relative attributes(d_(ij), α_(ij), β_(ij)) between the reference minutia 210 and theneighboring minutia 220.

As depicted in FIG. 2, the automatic fingerprint identification system100 may compute a Euclidean distance 230 between the reference minutia210 and the neighboring minutia 220, a minimum rotation angle 240 forthe neighboring minutia, and a minimum rotation angle 250 for thereference minutia 210. In addition, the automatic fingerprintidentification system may compute a ridge count 260 across the referenceminutia 210 and the neighboring minutia 220.

Specifically, the rotation and translation invariant feature vector maybe represented as vector 1 (represented below). In some implementations,an OFV is created to describe the geometric relationship between.Further, M_(i) represents a reference minutia and M_(j) represents anearest neighbor minutiae in one of the octant sectors. The OFV for eachsector may be described in the given vector from vector 1:

Vector 1: (d_(ij), α_(ij), B_(ij)),

-   -   where d_(ij) denotes the Euclidean distance 230,    -   where α=λ(θ_(i), θ_(j)) denotes the minimum rotation angle 240        required to rotate a line of direction θ_(i) in a particular        direction (e.g., counterclockwise in FIG. 2) to make the line        parallel with a line of direction θ_(j), and    -   where β_(ij)=λ(θ_(i), ∠(M_(i),M_(j))) denotes the minimum        rotation angle 250, where ∠(M_(i),M_(j)) denotes the direction        from the reference minutia 210 to the neighboring minutia 220,        and where λ(a,b) denotes the same meaning as defined in α_(ij)

Specifically, because each element calculated in the feature vector is arelative measurement between the reference minutia 210 and theneighboring minutia 220, the feature vector is independent from therotation and translation of the fingerprint. Elements 230, 240, and 250may be referred to as relative features and are used to compute thesimilarity between pair of reference minutia 210 and the neighboringminutia 220. In some implementations, other minutiae features such asabsolute features may additionally or alternatively be used by theautomatic fingerprint identification system 100 to weight the computedsimilarity score between a pair of mated minutiae that includesreference minutia 210 and the neighboring minutia 220.

FIG. 3 is a graphical illustration of the relationships represented inan exemplary octant feature vector (OFV) 300. The OFV 300 may begenerated for a reference minutia 310, which corresponds to thereference minutia 210 as shown in FIG. 2. As shown, the OFV 300represents relationships between the reference minutiae 310 and itsnearest neighboring minutiae 322, 332, 342, 352, 362, 372, 382, and 392in sectors 320, 330, 340, 350, 360, 370, 380, and 390, respectively.However, the graphical illustration of OFV 300 does not depict thedetails of the geographic relationships, which are described withinrespect to FIG. 2. Although FIG. 3 indicates a neighboring minutiawithin each octant sector, in some instances, there may be noneighboring minutiae within a particular sector. In such instances, theOFV for the particular sector without a neighboring minutia is set tozero. Otherwise, because the neighboring minutiae 322, 332, 342, 352,362, 372, 382, and 392 may not overlap with reference minutiae 310, theOFV is greater than zero.

FIG. 4 is an exemplary process 400 of generating an octant featurevector (OFV). Briefly, the process 400 may include identifying aplurality of minutiae from the input fingerprint image (410), selectinga particular minutia from the plurality of minutiae (420), defining aset of octant sectors for the plurality of minutiae (430), assigningeach of the plurality of minutiae to an octant sector (440), identifyinga neighboring minutiae to the particular minutia for each octant sector(450), and generating an octant feature vector for the particularminutia (460).

In more detail, the process 400 may include the process may includeidentifying a plurality of minutiae from the input fingerprint image(410). For instance, the automatic fingerprint identification system 100may receive the input fingerprint 402 and generate a list of minutiae412 using the techniques described previously with respect to FIG. 1B.

The process 400 may include selecting a particular minutia from theplurality of minutiae (420). For instance, the automatic fingerprintidentification system 100 may select a particular minutiae within thelist of minutiae 412.

The process 400 may include defining a set of octant sectors for theplurality of minutiae (430). For instance, the automatic fingerprintidentification system 100 may generate a set of octant sectors 432 thatinclude individual octant sectors k₀ to k₇ as shown in FIG. 4. The setof octant sectors 432 may be generated in reference to the particularminutia that is selected in step 420.

The process 400 may include assigning each of the plurality of minutiaeto an octant sector (440). For instance, the automatic fingerprintidentification system 100 may assign each of the plurality of minutiaefrom the list of minutiae 412 into corresponding octant sectors withinthe set of octant sectors 432. The assigned minutiae may be associatedwith the corresponding octant sectors in a list 442 that includes thenumber of minutiae that are identified within each individual octantsector. For example, as shown in FIG. 4, the exemplary octant sector k₁has no identified minutiae, whereas the exemplary k₆ includes twoidentified minutiae within the octant sector. The graphical illustration444 represents the locations of the plurality of minutiae, relative tothe particular selected minutia, M_(i), within the individual octantsectors.

The process 400 may include identifying a neighboring minutiae to theparticular minutia for each octant sector (450). For instance, theautomatic fingerprint identification system 100 may identify, from allthe neighboring minutiae within each octant sector, the neighboringminutia that is the closest neighboring minutia based on the distancebetween each neighboring minutia and the particular selected minutia,Mi. For example, for the octant sector k₆, the automatic fingerprintidentification system 100 may determine that the minutia, M₆ is theclosest neighboring minutia based on the distance between M_(i) and M₆.The closest neighboring minutiae for all of the octant sectors may beaggregated within a list of closest neighboring minutiae 452 thatidentifies each of the closest neighboring minutiae.

The process 400 may include generating an octant feature vector for theparticular minutia (460). For instance, the automatic fingerprintidentification system 100 may generate an octant feature vector 462,based on the list of closest neighboring minutiae 452, which includes aset of relative features such as the Euclidean distance 230, the minimumrotation angle 240, and the minimum rotation angle 250 as describedpreviously with respect to FIG. 2.

As described above with respect to FIGS. 2-4, OFVs for minutiae may beused to characterize local relationships with neighboring minutiae,which are invariant to the rotation and translation of the fingerprintthat includes the minutiae. The OFVs are also insensitive to distortion,since the nearest neighboring minutiae are assigned to multiple octantsectors in various directions, thereby allowing flexibility oforientation of up to 45°. In this regard, the OFVs of minutiae within afingerprint may be compared against the OFVs of minutiae within anotherfingerprint (e.g., a search fingerprint) to determine a potential matchbetween the two fingerprints. Descriptions of the general fingerprintmatching process, and the OFV matching process are provided below.

Fingerprint Identification and Matching

In general, the automatic fingerprint identification system 100 mayperform fingerprint identification and matching in two stages: (1) anenrollment stage, and (2) an identification/verification stage.

In the enrollment stage, an individual (or a “registrant”) has theirfingerprints and personal information enrolled. The registrant may be anindividual manually providing their fingerprints for scanning or,alternately, an individual whose fingerprints were obtained by othermeans. In some examples, registrants may enroll fingerprints usinglatent prints, libraries of fingerprints, and any other suitablerepositories and sources of fingerprints. As described, the process of“enrolling” and other related terms refer to providing biometricinformation (e.g., fingerprints) to an identification system (e.g., theautomatic fingerprint identification system 100).

The automatic fingerprint identification 100 system may extract featuressuch as minutiae from fingerprints. As described, “features” and relatedterms refer to characteristics of biometric information (e.g.,fingerprints) that may be used in matching, verification, andidentification processes. The automatic fingerprint identificationsystem 100 may create a reference record using the personal informationand the extracted features, and save the reference record into thedatabase 150 for subsequent fingerprint matching, verification, andidentification processes.

In some implementations, the automatic fingerprint identification system100 may contain millions of reference records. As a result, by enrollinga plurality of registrants (and their associated fingerprints andpersonal information), the automatic fingerprint identification system100 may create and store a library of reference records that may be usedfor comparison to search records. The library may be stored at thedatabase 150 associated.

In the identification stage, the automatic fingerprint identificationsystem 100 may use the extracted features and personal information togenerate a record known as a “search record”. The search recordrepresents a source fingerprint for which identification is sought. Forexample, in criminal investigations, a search record may be retrievedfrom a latent print at a crime scene. The automatic fingerprintidentification may compare the search record with the enrolled referencerecords in the database 150. For example, during a search procedure, asearch record may be compared against the reference records stored inthe database 150. In such an example, the features of the search recordmay be compared to the features of each of the plurality of referencerecords. For instance, minutiae extracted from the search record may becompared to minutiae extracted from each of the plurality of referencerecords.

As described, a “similarity score” is a measurement of the similarity ofthe fingerprint features (e.g., minutiae) between the search record andeach reference record, represented as a numerical value to degree ofsimilarity. For instance, in some implementations, the values of thesimilarity score may range from 0.0 to 1.0, where a higher magnituderepresents a greater degree of similarity between the search record andthe reference record.

The automatic fingerprint identification system 100 may computeindividual similarity scores for each comparison of features (e.g.,minutiae), and aggregate similarity scores (or “final similarityscores”) between the search record to each of the plurality of referencerecords. In this regard, the automatic fingerprint identification system100 may generate similarity scores of varying levels of specificitythroughout the matching process of the search record and the pluralityof reference records.

The automatic fingerprint identification system 100 may also sort eachof the individual similarity scores based on the value of the respectivesimilarity scores of individual features. For instance, the automaticidentification system 100 may compute individual similarity scoresbetween respective minutiae between the search fingerprint and thereference fingerprint, and sort the individual similarity scores bytheir respective values.

A higher final similarity score indicates a greater overall similaritybetween the search record and a reference record while a lower finalsimilarity score indicates a lesser over similarity between the searchrecord and a reference record. Therefore, the match (e.g., therelationship between the search record and a reference record) with thehighest final similarity score is the match with the greatestrelationship (based on minutiae comparison) between the search recordand the reference record.

Minutiae and OFV Matching

In general, the OFVs of minutiae may be compared between twofingerprints to determine a potential match between a referencefingerprint and a search fingerprint. The automatic fingerprintidentification system 100 may compare the OFVs of corresponding minutiaefrom the reference fingerprint and the search fingerprint to compute anindividual similarity score that reflects a confidence that theparticular reference minutiae corresponds to the particular searchminutiae that is being compared to. The automatic fingerprintidentification system 100 may then compute aggregate similarity scores,between a list of reference minutiae and a list of search minutiae,based the values of the individual similarity scores for each minutiae.For instance, as described more particularly below, various types ofaggregation techniques may be used to determine the aggregate similarityscores between the reference fingerprint and the search fingerprint.

FIGS. 5-7 generally describe different processes that may be used toduring fingerprint identification and matching procedures. For instance,FIG. 5 illustrates an exemplary process of calculating an individualsimilarity score between a reference minutia and a search minutia. FIG.6 illustrates an exemplary alignment process between two fingerprintsusing extracted minutiae from the two fingerprints, and FIG. 7illustrates an exemplary minutiae matching technique that may beemployed after the alignment procedure represented in FIG. 7. Asdescribed with respect to FIG. 8, the processes represented in FIGS. 5-7may be used in conjunction during an exemplary distorted fingerprintmatching process.

Referring to FIG. 5, a similarity determination process 500 may be usedto compute an individual similarity score between a reference minutia502 a from a reference fingerprint, and a search minutia 502 b from asearch fingerprint. A reference OFV 504 a and a search OFV 504 b may begenerated for the reference minutia 502 a and the search minutia 504 b,respectively, using the techniques described with respect to FIG. 4. Asshown, the search and reference OFVs 504 a and 504 b include individualoctant sectors k₀ to k₇ as described as illustrated in FIG. 3. Each ofthe reference OFV 504 a and the search OFV 504 b may include parameterssuch as, for example, the Euclidian distance 230, the minimum rotationangle 240, and the minimum rotation angle 250 as described with respectto FIG. 2. As shown in FIG. 5, exemplary parameters 506 a and 506 b mayrepresent the parameters for octant sector k₀ of the reference OFV 502 aand the search OFV 502 b, respectively.

The similarity score determination may be performed by an OFV comparisonmodule 520, which may be a software module or component of the automaticfingerprint identification system 100. In general, the similarity scorecalculation includes four steps. Initially, the Euclidian distancevalues of a particular octant sector may be evaluated (522).Corresponding sectors between the reference OFV 504 a and the similarityOFV 504 b may then be compared (524). A similarity score between aparticular octant sector within the reference OFV 504 a and itscorresponding octant sector in the search OFV 504 b may then be computed(526). Finally, the similarity scores for the between other octantsectors of the reference OFV 504 a and the search OFV 504 b may then becomputed and combined to generate the final similarity score between thereference OFV 504 a and the search OFV 504 b (528).

With respect to step 522, the similarity module 520 may initiallydetermine if the Euclidian distance values within the parameters 506 aand 506 b are non-zero values. For instance, as shown in decision point522 a, the Euclidean distance associated with the octant sector of thereference OFV 504 a, d_(RO), is initially be evaluated. If this value isequal to zero, then the similarity score for the octant sector k₀ is setto zero. Alternatively, if the value of d_(RO) is greater than zero,then the similarity module 520 proceeds to decision point 522 b, wherethe Euclidean distance associated with the octant sector of thereference OFV 504 a, d_(SO), is evaluated. If the value of d_(SO) is notgreater than zero, then the OFV similarity module 520 evaluates thevalue of the Euclidean distance d_(S1), which is included in an adjacentsector k₁ to the octant sector k₀ within the reference OFV 504 b.Although FIG. 5 represents only one of the adjacent sectors beingselected, because each octant sector includes two adjacent octantsectors as shown in FIG. 3, in other implementations, octant sector k₇may also be evaluated. If the value of the Euclidean distance within theadjacent octant sector is not greater than zero, then the similaritymodule 520 sets the value of individual similarity score S_(RS01),between the octant sector k₀ of the reference OFV 504 a and the octantsector k₁ of the reference OFV 504 b, to zero.

Alternatively, if the either the value of Euclidean distance d_(RO)within the octant sector k₀ of the reference OFV 504 a, or the Euclideandistance d_(S1) within the adjacent octant sector k₁ of the search OFV504 b is determined to be a non-zero value within the decision points522 b and 522 c, respectively, then the similarity module proceeds tostep 524.

In some instances, a particular octant vector may include zerocorresponding minutiae within the search OFV 504 b due to localizeddistortions within the search fingerprint. In such instances, where thecorresponding minutiae may have drifted to an adjacent octant sector,the similarity module 520 may alternatively compare the features of theoctant vector of the reference OFV 504 a to a corresponding adjacentoctant vector of the search OFV 504 b as shown in step 524 b.

If proceeding through decision point 522 b, the similarity module 520may proceed to step 524 a where the corresponding octant sectors betweenthe reference OFV 504 a and the search OFV 504 b are compared. Ifproceeding through decision point 522 c, the similarity module 620 mayproceed to step 524 b where the octant sector k₀ of the reference OFV504 a is compared to the corresponding adjacent octant sector k₁ thesearch OFV 504 b. During either process, the similarity module 520 maycompute the difference between the parameters that are included withineach octant sector of the respective OFVs. For instance, as shown, thedifference between the Euclidean distances 230, Δd, the differencebetween the minimum rotation angles 240, Δα, and the difference betweenthe minimum rotation angles 250, Δβ, may be computed. Since theseparameters represent geometric relationships between pairs of minutiae,the differences between them represent distance and orientationdifferences between the reference and search minutiae with respect toparticular octant sectors.

In some implementations, dynamic threshold values for the computedfeature differences may be used to handle nonlinear distortions withinthe search fingerprint in order to find mated minutiae between thesearch and reference fingerprint. For instance, the values of thedynamic thresholds may be adjusted to larger or smaller values to adjustthe sensitivity of the mated minutiae determination process. Forexample, if the value of the threshold for the Euclidean distance is setto a higher value, than more minutiae within a particular octant sectormay be determined to be a neighboring minutia to a reference minutiaebased on the distance being lower than the threshold value. Likewise, ifthe threshold is set to a lower value, then a smaller number of minutiaewithin the particular octant sector may be determined to be neighboringminutia based on the distance to the reference minutia being greaterthan the threshold value.

After either comparing the corresponding octant sectors in step 524 a orcomparing the corresponding adjacent octant sectors in step 524 b, thesimilarity module 520 may then compute an individual similarity scorebetween the respective octant sectors in steps 526 a and 526 b,respectively. For instance, the similarity score may be computed basedon the values of the feature differences as computed in steps 524 a and524 b. For instance, the similarity score may represent the featuredifferences and indicate minutiae that are likely to be distortedminutiae. For example, if the feature differences between a referenceminutiae and corresponding search minutia within a particular octantsector are close the dynamic threshold values, the similarity module 520may identify the corresponding search minutia as a distortion candidate.

After computing the similarity score for either the corresponding octantsectors, or the corresponding adjacent sectors in steps 526 a and 526 b,respectively, the similarity module 520 may repeat the steps 522-526 forall of the other octant sectors included within the reference OFV 504 aand the search OFV 504 b. For instance, the similarity module 520 mayiteratively execute the steps 522-526 until the similarity scoresbetween each corresponding octant sector and each corresponding adjacentoctant sector are computed for the reference OFV 504 a and the searchOFV 504 b.

The similarity module may then combine the respective similarity scoresfor each corresponding octant sectors and/or the corresponding adjacentoctant sectors to generate a final similarity score between thereference minutia and the corresponding search minutia. This finalsimilarity score is also referred to as the “individual similarityscore” between corresponding minutiae within the search and referencefingerprints as described in other sections of this specification. Theindividual similarity score indicates a strength of the local matchingof the corresponding OFV.

In some implementations, the particular aggregation technique used bythe similarity module 522 to generate the final similarity score (or the“individual similarity score”) may vary. For example, in some instances,the final similarity score may be computed based on adding the values ofthe similarity scores for the corresponding octant sectors and thecorresponding adjacent octant sectors, and normalizing the sum by a sumof a total number of possible mated minutiae for the reference minutiaand a total of number of possible mated minutiae for the search minutia.In this regard, the final similarity score is weighted by consideringthe number of mated minutiae and the total number of possible matedminutiae.

FIG. 6 illustrates an exemplary alignment process 600 between areference fingerprint and a search fingerprint. Briefly, the process 600may initially compare a list of reference OFVs 604 a associated with alist of reference minutiae 602 a and a list of search OFVs 604 bassociated with a list of search minutiae 604 b, and generate a list ofall possible mated minutiae 612. A global alignment module 620 may thenperform a global alignment procedure on the list of all possible matedminutia 612 to generate a clustered list of all possible mated minutiae622, and determine two best alignment rotations 622 for the searchfingerprint relative to the reference fingerprint. A precision alignmentmodule may then use the two best rotations 624 perform a secondalignment procedure to generate a list that includes the best-alignedpair 632 for the plurality of bins, which are provided as outputs of thealignment process.

As described previously with respect to FIG. 5, the OFVs ofcorresponding minutiae within the reference fingerprint and the searchfingerprint may be compared by the OFV comparison module 610 to generatethe list of all possible mated minutiae 612. As described, “matedminutiae” refer to a pair of minutiae that includes a particularreference minutia and a corresponding search minutia based at least onthe OFV comparison performed by the OFV comparison module 610, and thevalue of the individual similarity score between the two respective OFVsof the reference and search minutiae. The individual similarity scoreindicates a strength of the local matching of the corresponding OFV. Inaddition, the list of all possible mated minutiae 612 includes all ofthe minutiae within the octant sectors that are identified asneighboring minutiae to a particular reference minutia and have anon-zero similarity score, although additional mated minutiae may existwith similarity score values equal to zero. Although as shown in theFIG., the list of all possible minutiae 612 includes one search minutiaper reference minutia, in some instances, multiple mated minutiae mayexist within the list of all possible minutiae 612 for a singlereference minutia.

The global alignment module 620 performs a global alignment process onthe list of all possible mated minutiae 612, which estimates a probable(or best rotation) alignment between the reference fingerprint and thesearch fingerprint based on comparing the angle offsets between theindividual minutiae within the mated minutiae. For instance, the globalalignment module 620 may initially compute an angle offset for eachmated minutiae pair based on the individual similarity scores between aparticular reference minutia and its corresponding search minutia.

Each of the mated minutiae within the list of all possible matedminutiae 612 may then be grouped into a historical bin that isassociated with a particular angle offset range. For instance, two matedminutiae pairs within the list of all possible mated minutiae 612 may begrouped into the same historical bin if their respective individualsimilarity scores indicate a similar angular offset between theindividual minutia within each mated minutiae pair. In some instances,the number of historical bins for the list of all possible matedminutiae 612 is used to estimate a fingerprint quality score for thesearch fingerprint. For example, if the quality of the searchfingerprint is excellent, then the angular offset among each of themated minutiae within the list of all possible mated minutiae 612 shouldbe consistent, and majority of the mated minutiae will be grouped into asingle historical bin. Alternatively, if the fingerprint quality ispoor, then the number of historical bins would increase, representingsignificant variations between the angular offset values between themated minutiae within the list of all possible mated minutiae 612.

In addition to grouping the mated minutiae into a particular historicalbin, the global alignment module 620 may determine a set of rigidtransformation parameters, which indicate geometric differences betweenthe reference minutiae and the search minutiae with similar angularoffsets. The rigid transformation parameters thus indicate a necessaryrotation of the search fingerprint at particular locations, representedby the locations of the minutiae, in order to geometrically align thesearch fingerprint to the reference fingerprint. Since the rigidtransformation parameters are computed for all possible mated minutiae,the necessary rotation represents a global alignment between thereference fingerprint and the search fingerprint. The global alignmentmodule 620 may then generate a clustered list of all possible matedminutiae, which groups the mated minutiae by the historical bin based onthe respective angle offsets, and includes a set of rigid transformationparameters. In some implementations, the histogram represented by theplurality of bins may be smoothened by a Gaussian function.

The global alignment module 620 may use the clustered list of allpossible mated minutiae to determine two best rotations 624. Forinstance, the two best rotations 624 may be determined by using therigid transformation parameters to calculate a set of alignmentrotations for the search fingerprint using each historical bin as areference point. Each alignment rotation may then be applied to thesearch fingerprint to generate a plurality of transformed searchfingerprints that is individually mapped to each alignment rotation. Forexample, in some instances, the number of alignment rotationscorresponds to the number of historical bins generated for the list ofall possible mated minutiae 612. In such instances, the number oftransformed search fingerprints generated corresponds to the number ofhistorical bins included in the cluster list of all possible matedminutiae 622. Each set of transformed search fingerprints may then becompared to the reference fingerprint to determine the two bestrotations 624. For example, as described more particularly with respectto FIG. 7, each transformed search fingerprint may be compared to thereference fingerprint using a minutiae matching technique to determinewhich particular alignment rotations generate the greatest number ofcorrectly matched minutiae between a particular transformed searchfingerprint and the reference fingerprint. The global alignment module620 may then extract the two best alignment rotations 624, which arethen used by the precision alignment module 630.

In some implementations, different matching constraints may be used withthe minutiae matching techniques to determine the two best alignmentrotations 624.

The precision alignment module 630 may then use the two best alignmentrotations 624 to perform a precision alignment process that iterativelyrotates individual minutiae within the search fingerprint around the twobest alignment rotations 632 several times with small angle variationsto obtain a more precise pairing between individual search minutiae andtheir corresponding reference minutiae. For example, in some, twelverotations may be used with two degree angle variations. The minutiaethat are associated with the precise pairing between the searchfingerprint and the reference fingerprint are determined to be the listof best-aligned minutiae 632, which are provided for output by theprocess 600. The list of best-aligned minutiae 632 representtransformations of individual search minutiae within the searchfingerprint that most closely pair with the corresponding referenceminutiae of the reference fingerprint as a result of the globalalignment and the precision alignment processes.

FIG. 7 illustrates an exemplary minutiae matching process 700. Theminutiae matching process 700 may be performed after the fingerprintalignment process 600 as described in FIG. 6 to remove falsecorrespondences included within a list of aligned minutiae that isoutputted from alignment process. For instance, fingerprints from twofingers of an individual may share local structures, which can result infalse correspondence minutiae between a search fingerprint of one fingerand a reference fingerprint of another finger. To resolve this, theminutiae matching process 700 includes a two-stage pruning process toremove false correspondence minutiae pairs within a list of all possiblemated minutiae.

Briefly, the process 700 may include a local geometric module 710receiving a list of aligned minutiae 710, and generating a modified listof all possible minutiae 712 that does not include false correspondenceminutiae. A global consistency pairing module may then sort the modifiedlist of all possible minutiae 712 by values of the respective individualsimilarity scores to generate a sorted modified list of all possibleminutiae 722. The global consistency pairing module 720 may removeminutiae pairs with duplicate indexes 724, and group the list of matedminutiae based on conducting a global geometric consistency evaluationto generate a list of geometrically consistent groups 726, which arethen outputted with a top average similarity score from one of thegeometrically consistent groups.

Initially, the local geometric module 710 may select the best-pairedminutiae from the list of all possible mated minutiae. For instance,after aligned the search fingerprint and the reference fingerprint asdescribed in FIG. 6, the local geometric module 710 may scan the list ofall possible minutiae and identify the two minutiae pairs with theminimum orientation difference.

In some instances, the identification of the best-paired minutiae mayadditionally be subject to satisfying a set of constraints. For example,one constraint may be that the index of the first pair is different fromthat of the first pair. In other examples, the rigid transformationparameters of the two best-paired minutiae pairs may be compared tothreshold values to ensure that the two identified pairs aregeometrically consistent.

After identifying the two best-paired minutiae pairs, the localgeometric module 710 may use the two best-paired minutiae pairs asreference pairs to remove other minutiae pairs from the list of allpossible mated minutiae. In some instances, particular pairs may beremoved if they satisfy one or more removal criteria based on theattributes of the two best paired minutiae pairs. For example, oneconstraint may be that if the minutiae index of a particular pair is thesame as one of the best-paired minutiae pairs, then that particular pairmay be identified as a duplicate within the list of all possibleminutiae and removed as a false correspondence. In another example, arotational constraint may be used to remove particular minutiae pairsthat have a large orientation difference compared to the two best-pairedminutiae pairs. In another example, distance constraints may be used tokeep each particular minutiae pair within the list of all possible matedminutiae geometrically consistent with the two best-paired minutiaepairs. The updated list of minutiae pairs that is generated is themodified list of all possible mated minutiae 712.

After the modified list of all possible mated minutiae is generated, theglobal consistency pairing module 720 may perform a global consistencypairing operation on the modified list of all possible mated minutiae722 to generate the list of geometrically-consistent groups 726 (or“globally aligned mated minutiae”). For instance, the global consistencypairing module 720 may initially sort the list modified list of allpossible mated minutiae 712 by the similarity score, and then scan thelist and remove particular minutiae pairs 724 that have minutiae indexesthat are similar to the minutiae pairs with the highest similarityscores in the sorted modified list of all possible mated minutiae 722.

The global consistency pairing module 720 may then initialize a set ofgroups based on the number of reference minutiae included within thelist. For instance, a group may be created for each reference minutiasuch that if there are multiple minutiae pairs within the list of sortedmodified list of all possible minutiae 722 for a single referenceminutiae, the multiple minutiae pairs are included in the same group. Insome instances, the global consistency pairing module 720 mayadditionally check the geometric consistency between each of theminutiae pairs within the same group and remove minutiae pairs that aredetermined not be geometrically consistent. The global consistencypairing module 720 may then compute an average similarity score for eachgroup based on aggregating the individual similarity scores associatedwith each of the minutiae pairs within the group.

After computing the average similarity scores for each group, the globalconsistency pairing module 720 may then compare the average similarityscores between each group and select the group that has the highestaverage similarity score and then provide the list of minutiae that areincluded within the group for output of the process 700 and include thetop average similarity score.

As describe above, FIGS. 5-7 illustrate processes that are utilized bythe automatic fingerprint identification system to process, analyze, andmatch individual minutiae from a search fingerprint to a referencefingerprint. As described in FIG. 8, these processes may be utilizedwithin a matching operation for a distorted fingerprint to compute afinal similarity score that indicates a confidence of a fingerprintmatch between a search fingerprint and a reference fingerprint.

Distorted Fingerprint Matching

FIG. 8 illustrates an exemplary distorted fingerprint matching operation800. The fingerprint matching operation 800 may be used to match asearch fingerprint that may be nonlinearly distorted or may exhibitother types of errors that would like to cause inaccurate results usingcontemporary fingerprint matching technologies. The fingerprint matchingoperation 800 employs the principles and techniques described previouslyin FIGS. 1-7 to reduce errors resulting from distortion.

The fingerprint matching operation 800 may include generating a list ofinvariant and distortion-tolerant reference OFVs 802 a for a list ofreference minutiae 802 a, and a list of invariant anddistortion-toleration search OFVs 804 b for a list of search minutiae802 a. For instance, as described in FIG. 4, the list of reference OFVs804 a and the list of search OFVs 804 b may be generated by comparingeach of the list of search minutiae 802 a and the list of reference 802b to its neighboring minutiae within a set of octant sectors.

An OFV comparison module may compare the respective search and referenceOFVs 804 a and 804 b, respectively, and generate a list of all possiblemated minutiae 812. For instance, as described in FIGS. 6-7, the list ofall possible mated minutiae may include individual pairs of searchminutiae and corresponding reference minutiae based on comparing thefeatures included within the respective reference OFVs included in thelist of reference OFVs 804 a and the respective search OFVs included inthe list of search OFVs 804 b. The list of reference OFVs 804 a and thelist of search OFVs 804 b may include a set of absolute features thatimprove fingerprint matching for distorted fingerprint matching. Forinstance, the absolute features may include a direction, x-ycoordinates, a quality score, a ridge frequency, and a curvature. Insome implementations, the absolute features are included into thecalculation of individual similarity scores as described in FIG. 5 asweights to factor in impacts of distortions on the search fingerprint.

A global alignment module 820 may cluster all of the list of allpossible mated minutiae pairs into a plurality of histogram bins, andsubsequently perform a first global alignment procedure on eachhistorical bin to determine two best rotations 822 for the list of allpossible mated minutiae 812. For instance, as described in FIG. 6, theglobal alignment module 820 may initially cluster the list of allpossible mated minutiae 812 based on a range of rotation angles,determine the two best rotations 822 based on comparing the each of thetransformed search fingerprint under each rotation angle to thereference fingerprint.

A precision alignment module 830 may use the two best rotations 822 toidentify a list of best-aligned minutiae 832 within the list of allpossible mated minutiae. The precision alignment module 830 may alsoperform a two-step pruning procedure to generate a list ofgeometrically-consistent groups 834. For instance, as described in FIG.7, the two-step pruning procedure may include performing a localgeometric pairing operation by removing false correspondence minutiaefrom the list of all possible mated minutiae.

A similarity module 840 may compute an individual similarity score foreach mated minutiae pair within the list of geometrically-consistentgroups 834. For instance, as described in FIG. 5, the individualsimilarity score may computed based on aggregating single similarityscores between individual octant sectors of the respective OFVs of thesearch and reference minutia within a mated minutiae pair. Thesimilarity score calculation may additionally include wrights based onthe absolute features included within the respective OFVs for the searchand reference minutiae included within the list of all possible matedminutiae. The list of individual similarity scores for all matedminutiae may then be aggregated to compute a final similarity scorebetween the reference fingerprint and the search fingerprint. In someinstances, the final similarity score is computed based on selecting themaximum value of the individual similarity score for all of the matedminutiae.

In some implementations, a distortion flag may additionally be set afterthe final similarity score is computed based on the number of matedminutiae that are identified as distortion minutiae. In some instances,the number of distorted minutiae may be compared to a threshold value todetermine whether the final similarity score may be severely distorted.In such instances, a distortion notification may be provided to anend-user of the automatic fingerprint identification system 100indicating that the final similarity score may be distorted and requiresmanual verification or re-enrollment of either the search or referencefingerprints.

It should be understood that processor as used herein means one or moreprocessing units (e.g., in a multi-core configuration). The termprocessing unit, as used herein, refers to microprocessors,microcontrollers, reduced instruction set circuits (RISC), applicationspecific integrated circuits (ASIC), logic circuits, and any othercircuit or device capable of executing instructions to perform functionsdescribed herein.

It should be understood that references to memory mean one or moredevices operable to enable information such as processor-executableinstructions and/or other data to be stored and/or retrieved. Memory mayinclude one or more computer readable media, such as, withoutlimitation, hard disk storage, optical drive/disk storage, removabledisk storage, flash memory, non-volatile memory, ROM, EEPROM, randomaccess memory (RAM), and the like.

Additionally, it should be understood that communicatively coupledcomponents may be in communication through being integrated on the sameprinted circuit board (PCB), in communication through a bus, throughshared memory, through a wired or wireless data communication network,and/or other means of data communication.

Additionally, it should be understood that data communication networksreferred to herein may be implemented using Transport ControlProtocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), orthe like, and the underlying connections may comprise wired connectionsand corresponding protocols, for example, Institute of Electrical andElectronics Engineers (IEEE) 802.3 and/or wireless connections andassociated protocols, for example, an IEEE 802.11 protocol, an IEEE802.15 protocol, and/or an IEEE 802.16 protocol.

A technical effect of systems and methods described herein includes atleast one of: (a) increased accuracy in facial matching systems; (b)reduction of false accept rate (FAR) in facial matching; (c) increasedspeed of facial matching.

Although specific features of various embodiments of the invention maybe shown in some drawings and not in others, this is for convenienceonly. In accordance with the principles of the invention, any feature ofa drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

What is claimed is:
 1. A method for matching distorted fingerprintsimplemented by an automatic fingerprint identification system includinga processor, a memory coupled to the processor, an interface to afingerprint scanning device, and a sensor associated with thefingerprint scanning device that indicates a fingerprint match, thecomputer-implemented method comprising: receiving (i) a plurality ofsearch octant feature vectors associated with a plurality of searchminutiae extracted from a search fingerprint, and (ii) a plurality ofreference octant feature vectors associated with a plurality ofreference minutiae extracted from a reference fingerprint, wherein: thesearch fingerprint includes one or more non-overlapping sectors thateach include a subset of the plurality of search minutiae, and each ofthe plurality of search octant feature vectors includes a set ofrelative features that represent a difference between a particularsearch minutia and a plurality of particular reference minutiae that areidentified as closest neighboring minutiae within a particular sectorthat includes the particular search minutiae; each of the plurality ofreference octant feature vectors includes a set of relative featuresthat represent a difference between a particular reference minutia and aplurality of particular search minutiae that are identified as closestneighboring minutiae within a particular sector that includes theparticular search minutiae; computing, for each of the plurality ofsearch minutiae, a first score that (i) represents the differencebetween the particular search octant feature associated with theparticular search minutia and the plurality of reference octant featurevectors associated with a plurality of particular reference minutiaethat are identified as closest neighboring minutiae within theparticular sector that includes the particular search minutiae, and (ii)is adjusted based at least on determining that at least one of theplurality of particular reference minutiae that are identified as theclosest neighboring minutiae within the particular sector that includesthe particular search minutia has drifted to another of the one or moresectors due to distortion within the search fingerprint; computing, foreach of the plurality of search minutiae, a second score that representsa ratio indicating a number of paired mated minutiae relative to anumber of paired unmated minutiae all of the one or more sectors,wherein: the paired mated minutiae represent the particular searchminutia that have the plurality of particular reference minutiae thatare identified as closest neighboring minutiae within each of the one ormore sectors, and the paired unmated minutiae represent the particularsearch minutia that do not have at least one particular referenceminutia that is identified as the closest neighboring minutiae withineach of the one or more sectors; computing, for each of the plurality ofsearch minutiae, a local similarity score based at least on combiningthe first score and the second score; determining, based at least on thecomputed local similarity scores for each of the plurality of searchminutiae, (i) a geometrically aligned region between the searchfingerprint and the reference fingerprint, and (ii) a rotation anglebetween the search fingerprint and the reference fingerprint;identifying a plurality of globally aligned mated minutiae within thegeometrically aligned region, wherein the plurality of globally alignedmated minutiae represent a set of particular search minutia that havegeometrically consistent features, determined based at least on therotation angle, to a plurality of particular reference minutiae withinthe geometrically aligned region; computing, for each of the pluralityof globally aligned mated minutiae, an additional first score thatrepresents the difference between a particular search octant featureassociated with the particular search minutia within the geometricallyaligned region and a plurality of reference octant feature vectorsassociated with the plurality of particular reference minutiae withinthe geometrically aligned region; computing, for each of the pluralityof globally aligned mated minutiae, an additional second score thatrepresents a ratio indicating a number of paired globally aligned matedminutiae relative to a number of globally aligned unmated minutiaewithin geometrically aligned region; computing (i) a third score basedat least on combining the respective first scores for each of theplurality of search minutiae, and the respective additional first scoresfor each of the plurality of globally aligned mated minutiae, (ii) afourth score based at least on combining the respective second scoresfor each of the plurality of search minutiae, and the respectiveadditional second scores for each of the plurality of globally alignedmated minutiae; computing a match similarity score between the searchfingerprint and the reference fingerprint based at least on combiningthe third score and the fourth score; and providing the match similarityscore for output to the automatic fingerprint identification system.